
"""simple docstring"""






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:



					class 			lowercase					:






								@staticmethod
								def 					UpperCAmelCase							(*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	str:





									"""simple docstring"""


									pass




def __magic_name__	(				_lowerCamelCase:		Image			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  hashlib.mda(image.tobytes()			)
	return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			lowercase					( unittest.TestCase		):
			lowercase        =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_      ,image_processor=SCREAMING_SNAKE_CASE_			)
				return depth_estimator, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Any      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
				self.assertEqual({'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)}      ,SCREAMING_SNAKE_CASE_			)
				import datasets

				lowerCAmelCase												=  datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''			)
				lowerCAmelCase												=  depth_estimator(
				    [
				        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			),
				        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
				        # RGBA
				        dataset[0]['''file'''],
				        # LA
				        dataset[1]['''file'''],
				        # L
				        dataset[2]['''file'''],
				    ]			)
				self.assertEqual(
				    [
				        {'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)},
				        {'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)},
				        {'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)},
				        {'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)},
				        {'''predicted_depth''': ANY(torch.Tensor			), '''depth''': ANY(Image.Image			)},
				    ]      ,SCREAMING_SNAKE_CASE_      ,)






			@require_tf
			@unittest.skip('''Depth estimation is not implemented in TF'''			)
			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[int]:





				"""simple docstring"""


				pass






			@slow
			@require_torch
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''Intel/dpt-large'''
				lowerCAmelCase												=  pipeline('''depth-estimation'''      ,model=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
				lowerCAmelCase												=  hashimage(outputs['''depth''']			)

				# This seems flaky.
				# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
				self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()			)      ,29.3_04			)
				self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()			)      ,2.6_62			)






			@require_torch
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''			)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Dict      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


